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Fragment-Based Approaches and Computer-Aided Drug Discovery

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Part of the book series: Topics in Current Chemistry ((TOPCURRCHEM,volume 317))

Abstract

Fragment-based design has significantly modified drug discovery strategies and paradigms in the last decade. Besides technological advances and novel therapeutic avenues, one of the most significant changes brought by this new discipline has occurred in the minds of drug designers. Fragment-based approaches have markedly impacted rational computer-aided design both in method development and in applications. The present review illustrates the importance of molecular fragments in many aspects of rational ligand design, and discusses how thinking in “fragment space” has boosted computational biology and chemistry.

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References

  1. Shuker SB, Hajduk PJ, Meadows RP et al (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274:1531–1534

    Article  CAS  Google Scholar 

  2. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–715

    Article  CAS  Google Scholar 

  3. Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304–1351

    Article  CAS  Google Scholar 

  4. Schreiber SL (2000) Target-oriented and diversity-oriented organic synthesis in drug discovery. Science 287:1964–1969

    Article  CAS  Google Scholar 

  5. Pereira DA, Williams JA (2007) Origin and evolution of high throughput screening. Br J Pharmacol 152:53–61

    Article  CAS  Google Scholar 

  6. Lipinski CA, Lombardo F, Dominy BW et al (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26

    Article  CAS  Google Scholar 

  7. Hann MM, Leach AR, Harper G (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 41:856–864

    Article  CAS  Google Scholar 

  8. Congreve M, Marshall F (2010) The impact of GPCR structures on pharmacology and structure-based drug design. Br J Pharmacol 159:986–996

    Article  CAS  Google Scholar 

  9. Congreve M, Chessari G, Tisi D et al (2008) Recent developments in fragment-based drug discovery. J Med Chem 51:3661–3680

    Article  CAS  Google Scholar 

  10. Murray CW, Rees DC (2009) The rise of fragment-based drug discovery. Nat Chem 1:187–192

    Article  CAS  Google Scholar 

  11. Orita M, Warizaya M, Amano Y et al (2009) Advances in fragment-based drug discovery platforms. Expert Opin Drug Discov 4:1125–1144

    Article  CAS  Google Scholar 

  12. Warr WA (2009) Fragment-based drug discovery. J Comput Aided Mol Des 23:453–458

    Article  CAS  Google Scholar 

  13. Erlanson DA (2006) Fragment-based lead discovery: a chemical update. Curr Opin Biotechnol 17:643–652

    Article  CAS  Google Scholar 

  14. Hajduk PJ, Greer J (2007) A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov 6:211–219

    Article  CAS  Google Scholar 

  15. Law R, Barker O, Barker JJ et al (2009) The multiple roles of computational chemistry in fragment-based drug design. J Comput Aided Mol Des 23:459–473

    Article  CAS  Google Scholar 

  16. Jencks WP (1981) On the attribution and additivity of binding energies. Proc Natl Acad Sci USA 78:4046–4050

    Article  CAS  Google Scholar 

  17. Kuntz ID, Chen K, Sharp KA et al (1999) The maximal affinity of ligands. Proc Natl Acad Sci USA 96:9997–10002

    Article  CAS  Google Scholar 

  18. Hopkins AL, Groom CR, Alex A (2004) Ligand efficiency: a useful metric for lead selection. Drug Discov Today 9:430–431

    Article  Google Scholar 

  19. Reynolds CH, Bembenek SD, Tounge BA (2007) The role of molecular size in ligand efficiency. Bioorg Med Chem Lett 17:4258–4261

    Article  CAS  Google Scholar 

  20. Orita M, Ohno K, Niimi T (2009) Two ‘Golden Ratio’ indices in fragment-based drug discovery. Drug Discov Today 14:321–328

    Article  CAS  Google Scholar 

  21. Abad-Zapatero C, Perisic O, Wass J et al (2010) Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation. Drug Discov Today 15:804–811

    Article  CAS  Google Scholar 

  22. Murray CW, Verdonk ML (2002) The consequences of translational and rotational entropy lost by small molecules on binding to proteins. J Comput Aided Mol Des 16:741–753

    Article  CAS  Google Scholar 

  23. Borsi V, Calderone V, Fragai M et al (2010) Entropic contribution to the linking coefficient in fragment based drug design: a case study. J Med Chem 53:4285–4289

    Article  CAS  Google Scholar 

  24. Babaoglu K, Shoichet BK (2006) Deconstructing fragment-based inhibitor discovery. Nat Chem Biol 2:720–723

    Article  CAS  Google Scholar 

  25. Chung S, Parker JB, Bianchet M et al (2009) Impact of linker strain and flexibility in the design of a fragment-based inhibitor. Nat Chem Biol 5:407–413

    Article  CAS  Google Scholar 

  26. Blum LC, Reymond JL (2009) 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc 131:8732–8733

    Article  CAS  Google Scholar 

  27. Siegal G, Ab E, Schultz J (2007) Integration of fragment screening and library design. Drug Discov Today 12:1032–1039

    Article  CAS  Google Scholar 

  28. Congreve M, Carr R, Murray C et al (2003) A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today 8:876–877

    Article  Google Scholar 

  29. Baurin N, Aboul-Ela F, Barril X et al (2004) Design and characterization of libraries of molecular fragments for use in NMR screening against protein targets. J Chem Inf Comput Sci 44:2157–2166

    Article  CAS  Google Scholar 

  30. Schuffenhauer A, Ruedisser S, Marzinzik AL et al (2005) Library design for fragment based screening. Curr Top Med Chem 5:751–762

    Article  CAS  Google Scholar 

  31. Venhorst J, Núñez S, Kruse CG (2010) Design of a high fragment efficiency library by molecular graph theory. ACS Med Chem Lett 1:499–503

    Article  CAS  Google Scholar 

  32. Gianti E, Sartori L (2008) Identification and selection of “privileged fragments” suitable for primary screening. J Chem Inf Model 48:2129–2139

    Article  CAS  Google Scholar 

  33. Lewell XQ, Judd DB, Watson SP et al (1998) RECAP–retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci 38:511–522

    Article  CAS  Google Scholar 

  34. Maass P, Schulz-Gasch T, Stahl M et al (2007) Recore: a fast and versatile method for scaffold hopping based on small molecule crystal structure conformations. J Chem Inf Model 47:390–399

    Article  CAS  Google Scholar 

  35. Degen J, Wegscheid-Gerlach C, Zaliani A et al (2008) On the art of compiling and using ‘drug-like’ chemical fragment spaces. ChemMedChem 3:1503–1507

    Article  CAS  Google Scholar 

  36. Mauser H, Stahl M (2007) Chemical fragment spaces for de novo design. J Chem Inf Model 47:318–324

    Article  CAS  Google Scholar 

  37. Lameijer EW, Kok JN, Back T et al (2006) Mining a chemical database for fragment co-occurrence: discovery of “chemical cliches”. J Chem Inf Model 46:553–562

    Article  CAS  Google Scholar 

  38. Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1:727–730

    Article  CAS  Google Scholar 

  39. Hajduk PJ, Huth JR, Fesik SW (2005) Druggability indices for protein targets derived from NMR-based screening data. J Med Chem 48:2518–2525

    Article  CAS  Google Scholar 

  40. Ciulli A, Williams G, Smith AG et al (2006) Probing hot spots at protein-ligand binding sites: a fragment-based approach using biophysical methods. J Med Chem 49:4992–5000

    Article  CAS  Google Scholar 

  41. Mattos C, Ringe D (1996) Locating and characterizing binding sites on proteins. Nat Biotechnol 14:595–599

    Article  CAS  Google Scholar 

  42. Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857

    Article  CAS  Google Scholar 

  43. Rognan D, Scapozza L, Folkers G et al (1995) Rational design of nonnatural peptides as high-affinity ligands for the HLA-B*2705 human leukocyte antigen. Proc Natl Acad Sci USA 92:753–757

    Article  CAS  Google Scholar 

  44. von Itzstein M, Dyason JC, Oliver SW et al (1996) A study of the active site of influenza virus sialidase: an approach to the rational design of novel anti-influenza drugs. J Med Chem 39:388–391

    Article  Google Scholar 

  45. Miranker A, Karplus M (1991) Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 11:29–34

    Article  CAS  Google Scholar 

  46. Eisen MB, Wiley DC, Karplus M et al (1994) HOOK: a program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site. Proteins 19:199–221

    Article  CAS  Google Scholar 

  47. Schubert CR, Stultz CM (2009) The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design. J Comput Aided Mol Des 23:475–489

    Article  CAS  Google Scholar 

  48. Bohm HJ (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6:61–78

    Article  CAS  Google Scholar 

  49. Verdonk ML, Cole JC, Taylor R (1999) SuperStar: a knowledge-based approach for identifying interaction sites in proteins. J Mol Biol 289:1093–1108

    Article  CAS  Google Scholar 

  50. Dennis S, Kortvelyesi T, Vajda S (2002) Computational mapping identifies the binding sites of organic solvents on proteins. Proc Natl Acad Sci USA 99:4290–4295

    Article  CAS  Google Scholar 

  51. Brenke R, Kozakov D, Chuang GY et al (2009) Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques. Bioinformatics 25:621–627

    Article  CAS  Google Scholar 

  52. Chuang GY, Kozakov D, Brenke R et al (2009) Binding hot spots and amantadine orientation in the influenza a virus M2 proton channel. Biophys J 97:2846–2853

    Article  CAS  Google Scholar 

  53. Landon MR, Lieberman RL, Hoang QQ et al (2009) Detection of ligand binding hot spots on protein surfaces via fragment-based methods: application to DJ-1 and glucocerebrosidase. J Comput Aided Mol Des 23:491–500

    Article  CAS  Google Scholar 

  54. Guvench O, MacKerell AD Jr (2009) Computational fragment-based binding site identification by ligand competitive saturation. PLoS Comput Biol 5:e1000435

    Article  CAS  Google Scholar 

  55. Kasahara K, Kinoshita K, Takagi T (2010) Ligand-binding site prediction of proteins based on known fragment-fragment interactions. Bioinformatics 26:1493–1499

    Article  CAS  Google Scholar 

  56. Huang N, Jacobson MP (2010) Binding-site assessment by virtual fragment screening. PLoS One 5:e10109

    Article  CAS  Google Scholar 

  57. Irwin JJ, Shoichet BK (2005) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182

    Article  CAS  Google Scholar 

  58. Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49:377–389

    Article  CAS  Google Scholar 

  59. Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53:5858–5867

    Article  CAS  Google Scholar 

  60. Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4:649–663

    Article  CAS  Google Scholar 

  61. Loving K, Alberts I, Sherman W (2010) Computational approaches for fragment-based and de novo design. Curr Top Med Chem 10:14–32

    Article  CAS  Google Scholar 

  62. Moitessier N, Englebienne P, Lee D et al (2008) Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol 153(Suppl 1):S7–S26

    CAS  Google Scholar 

  63. Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288

    Article  CAS  Google Scholar 

  64. B-Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14:394–400

    Article  CAS  Google Scholar 

  65. Rarey M, Kramer B, Lengauer T (1999) The particle concept: placing discrete water molecules during protein-ligand docking predictions. Proteins 34:17–28

    Article  CAS  Google Scholar 

  66. Klebe G, Mietzner T (1994) A fast and efficient method to generate biologically relevant conformations. J Comput Aided Mol Des 8:583–606

    Article  CAS  Google Scholar 

  67. Sun Y, Ewing TJ, Skillman AG et al (1998) CombiDOCK: structure-based combinatorial docking and library design. J Comput Aided Mol Des 12:597–604

    Article  CAS  Google Scholar 

  68. Zsoldos Z, Reid D, Simon A et al (2007) eHiTS: a new fast, exhaustive flexible ligand docking system. J Mol Graph Model 26:198–212

    Article  CAS  Google Scholar 

  69. Huang D, Caflisch A (2010) Library screening by fragment-based docking. J Mol Recognit 23:183–193

    CAS  Google Scholar 

  70. Verdonk ML, Berdini V, Hartshorn MJ et al (2004) Virtual screening using protein-ligand docking: avoiding artificial enrichment. J Chem Inf Comput Sci 44:793–806

    Article  CAS  Google Scholar 

  71. Marcou G, Rognan D (2007) Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 47:195–207

    Article  CAS  Google Scholar 

  72. Li Y, Shen J, Sun X et al (2010) Accuracy assessment of protein-based docking programs against RNA targets. J Chem Inf Model 50:1134–1146

    Article  CAS  Google Scholar 

  73. Verdonk ML, Cole JC, Hartshorn MJ et al (2003) Improved protein-ligand docking using GOLD. Proteins 52:609–623

    Article  CAS  Google Scholar 

  74. Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  Google Scholar 

  75. Sandor M, Kiss R, Keseru GM (2010) Virtual fragment docking by Glide: a validation study on 190 protein-fragment complexes. J Chem Inf Model 50:1165–1172

    Article  CAS  Google Scholar 

  76. Boehm HJ, Boehringer M, Bur D et al (2000) Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. J Med Chem 43:2664–2674

    Article  CAS  Google Scholar 

  77. Makino S, Kayahara T, Tashiro K et al (2001) Discovery of a novel serine protease inhibitor utilizing a structure-based and experimental selection of fragments technique. J Comput Aided Mol Des 15:553–559

    Article  CAS  Google Scholar 

  78. Pickett SD, Sherborne BS, Wilkinson T et al (2003) Discovery of novel low molecular weight inhibitors of IMPDH via virtual needle screening. Bioorg Med Chem Lett 13:1691–1694

    Article  CAS  Google Scholar 

  79. Carbone V, Ishikura S, Hara A et al (2005) Structure-based discovery of human L-xylulose reductase inhibitors from database screening and molecular docking. Bioorg Med Chem 13:301–312

    Article  CAS  Google Scholar 

  80. Warner SL, Bashyam S, Vankayalapati H et al (2006) Identification of a lead small-molecule inhibitor of the Aurora kinases using a structure-assisted, fragment-based approach. Mol Cancer Ther 5:1764–1773

    Article  CAS  Google Scholar 

  81. Rummey C, Nordhoff S, Thiemann M et al (2006) In silico fragment-based discovery of DPP-IV S1 pocket binders. Bioorg Med Chem Lett 16:1405–1409

    Article  CAS  Google Scholar 

  82. Teotico DG, Babaoglu K, Rocklin GJ et al (2009) Docking for fragment inhibitors of AmpC beta-lactamase. Proc Natl Acad Sci USA 106:7455–7460

    Article  CAS  Google Scholar 

  83. Chen D, Misra M, Sower L et al (2008) Novel inhibitors of anthrax edema factor. Bioorg Med Chem 16:7225–7233

    Article  CAS  Google Scholar 

  84. Chen Y, Shoichet BK (2009) Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat Chem Biol 5:358–364

    Article  CAS  Google Scholar 

  85. McLean LR, Zhang Y, Li H et al (2010) Fragment screening of inhibitors for MIF tautomerase reveals a cryptic surface binding site. Bioorg Med Chem Lett 20:1821–1824

    Article  CAS  Google Scholar 

  86. Englert L, Silber K, Steuber H et al (2010) Fragment-based lead discovery: screening and optimizing fragments for thermolysin inhibition. ChemMedChem 5:930–940

    CAS  Google Scholar 

  87. Ruda GF, Campbell G, Alibu VP et al (2010) Virtual fragment screening for novel inhibitors of 6-phosphogluconate dehydrogenase. Bioorg Med Chem 18:5056–5062

    Article  CAS  Google Scholar 

  88. Rohrig UF, Awad L, Grosdidier A et al (2010) Rational design of indoleamine 2,3-dioxygenase inhibitors. J Med Chem 53:1172–1189

    Article  CAS  Google Scholar 

  89. Mortier J, Masereel B, Remouchamps C et al (2010) NF-kappaB inducing kinase (NIK) inhibitors: identification of new scaffolds using virtual screening. Bioorg Med Chem Lett 20:4515–4520

    Article  CAS  Google Scholar 

  90. Gleeson MP, Gleeson D (2009) QM/MM as a tool in fragment based drug discovery. A cross-docking, rescoring study of kinase inhibitors. J Chem Inf Model 49:1437–1448

    Article  CAS  Google Scholar 

  91. Graves AP, Shivakumar DM, Boyce SE et al (2008) Rescoring docking hit lists for model cavity sites: predictions and experimental testing. J Mol Biol 377:914–934

    Article  CAS  Google Scholar 

  92. Novikov FN, Stroylov VS, Stroganov OV et al (2010) Improving performance of docking-based virtual screening by structural filtration. J Mol Model 16:1223–1230

    Article  CAS  Google Scholar 

  93. Deng Z, Chuaqui C, Singh J (2004) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. J Med Chem 47:337–344

    Article  CAS  Google Scholar 

  94. Kelly MD, Mancera RL (2004) Expanded interaction fingerprint method for analyzing ligand binding modes in docking and structure-based drug design. J Chem Inf Comput Sci 44:1942–1951

    Article  CAS  Google Scholar 

  95. Mpamhanga CP, Chen B, McLay IM et al (2006) Knowledge-based interaction fingerprint scoring: a simple method for improving the effectiveness of fast scoring functions. J Chem Inf Model 46:686–698

    Article  CAS  Google Scholar 

  96. Venhorst J, Nunez S, Terpstra JW et al (2008) Assessment of scaffold hopping efficiency by use of molecular interaction fingerprints. J Med Chem 51:3222–3229

    Article  CAS  Google Scholar 

  97. Loving K, Salam NK, Sherman W (2009) Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation. J Comput Aided Mol Des 23:541–554

    Article  CAS  Google Scholar 

  98. Fukunishi Y, Mashimo T, Orita M et al (2009) In silico fragment screening by replica generation (FSRG) method for fragment-based drug design. J Chem Inf Model 49:925–933

    Article  CAS  Google Scholar 

  99. Li H, Li C (2010) Multiple ligand simultaneous docking: orchestrated dancing of ligands in binding sites of protein. J Comput Chem 31:2014–2022

    Article  CAS  Google Scholar 

  100. Babine RE, Bleckman TM, Kissinger CR et al (1995) Design, synthesis and X-ray crystallographic studies of novel FKBP-12 ligands. Bioorg Med Chem Lett 5:1719–1724

    Article  CAS  Google Scholar 

  101. Rich DH, Bohacek RS, Dales NA et al (1997) Transformation of peptides into non-peptides. Synthesis of computer-generated enzyme inhibitors. Chimia 51:45–47

    CAS  Google Scholar 

  102. Bohm HJ, Banner DW, Weber L (1999) Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J Comput Aided Mol Des 13:51–56

    Article  CAS  Google Scholar 

  103. Honma T, Hayashi K, Aoyama T et al (2001) Structure-based generation of a new class of potent Cdk4 inhibitors: new de novo design strategy and library design. J Med Chem 44:4615–4627

    Article  CAS  Google Scholar 

  104. Schneider G, Lee ML, Stahl M et al (2000) De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J Comput Aided Mol Des 14:487–494

    Article  CAS  Google Scholar 

  105. Grzybowski BA, Ishchenko AV, Kim CY et al (2002) Combinatorial computational method gives new picomolar ligands for a known enzyme. Proc Natl Acad Sci USA 99:1270–1273

    Article  CAS  Google Scholar 

  106. Ji H, Zhang W, Zhang M et al (2003) Structure-based de novo design, synthesis, and biological evaluation of non-azole inhibitors specific for lanosterol 14alpha-demethylase of fungi. J Med Chem 46:474–485

    Article  CAS  Google Scholar 

  107. Vinkers HM, de Jonge MR, Daeyaert FF et al (2003) SYNOPSIS: SYNthesize and OPtimize System in Silico. J Med Chem 46:2765–2773

    Article  CAS  Google Scholar 

  108. Rogers-Evans M, Alanine AI, Bleicher KH et al (2004) Identification of novel cannabinoid receptor ligands via evolutionary de novo design and rapid parallel synthesis. QSAR & Comb Sci 23:426–430

    Article  CAS  Google Scholar 

  109. Pierce AC, Rao G, Bemis GW (2004) BREED: Generating novel inhibitors through hybridization of known ligands. Application to CDK2, p38, and HIV protease. J Med Chem 47:2768–2775

    Article  CAS  Google Scholar 

  110. Krier M, Araujo-Junior JX, Schmitt M et al (2005) Design of small-sized libraries by combinatorial assembly of linkers and functional groups to a given scaffold: application to the structure-based optimization of a phosphodiesterase 4 inhibitor. J Med Chem 48:3816–3822

    Article  CAS  Google Scholar 

  111. Heikkila T, Thirumalairajan S, Davies M et al (2006) The first de novo designed inhibitors of Plasmodium falciparum dihydroorotate dehydrogenase. Bioorg Med Chem Lett 16:88–92

    Article  CAS  Google Scholar 

  112. Roche O, Rodriguez Sarmiento RM (2007) A new class of histamine H3 receptor antagonists derived from ligand based design. Bioorg Med Chem Lett 17:3670–3675

    Article  CAS  Google Scholar 

  113. Vieth M, Erickson J, Wang J et al (2009) Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches. J Med Chem 52:6456–6466

    Article  CAS  Google Scholar 

  114. Aronov AM, Bemis GW (2004) A minimalist approach to fragment-based ligand design using common rings and linkers: application to kinase inhibitors. Proteins 57:36–50

    Article  CAS  Google Scholar 

  115. Crisman TJ, Bender A, Milik M et al (2008) “Virtual fragment linking”: an approach to identify potent binders from low affinity fragment hits. J Med Chem 51:2481–2491

    Article  CAS  Google Scholar 

  116. Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11:1046–1053

    Article  CAS  Google Scholar 

  117. Bender A, Mussa HY, Gill GS et al (2004) Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT 3D). J Med Chem 47:6569–6583

    Article  CAS  Google Scholar 

  118. Nidhi GM, Davies JW et al (2006) Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model 46:1124–1133

    Article  CAS  Google Scholar 

  119. Clark M, Wiseman JS (2009) Fragment-based prediction of the clinical occurrence of long QT syndrome and torsade de pointes. J Chem Inf Model 49:2617–2626

    Article  CAS  Google Scholar 

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Rognan, D. (2011). Fragment-Based Approaches and Computer-Aided Drug Discovery. In: Davies, T., Hyvönen, M. (eds) Fragment-Based Drug Discovery and X-Ray Crystallography. Topics in Current Chemistry, vol 317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/128_2011_182

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